While various forms of online advertising have been used for several years, most types of conventional online advertising offer only limited relevance to consumers. The relevance technology behind traditional graphical ads, e.g. such as for banner ads, has changed little in the last decade.
Search-engine ads, such as those supplied by current search engine entities, e.g. Google and Overture, are typically more relevant than graphical ads, but are often less relevant than the search results provided by the associated search engines themselves.
Current online technology suffers from several significant limitations, including:                the use of cost-per-impression and cost-per-click pricing of the ads;        the complexity of keyword bidding;        the use of click rate for judging ad relevance; and        the number and type of features employed for judging relevance.        
The model of how ads are priced affects not only the economics of associated advertisers and publishers, but also the relevance of the ads to consumers.
Cost-per-impression (CPM) pricing is the oldest pricing model, and is still widely used for graphic ads. An advertiser pays a set rate each time an ad is shown to a consumer. CPM prices are typically negotiated for individual ads or ad campaigns with each publisher, by the publisher's sales force.
CPM-priced ads are rarely priced accurately to reflect their true value to advertisers, publishers, and consumers. For example, publishers' sales teams are motivated primarily by commissions that arise from closing deals and reducing sales costs, and thus ad inventory gets sold in large bundles that don't distinguish the relevance and value of individual ads. As well, advertisers are required to monitor the effectiveness of CPM ads, such as by tracking rates of clicks and conversions of clicks to purchases and/or actions, and most advertisers find it difficult to monitor performance effectively.
Cost-per-click (CPC) pricing currently accounts for more than half of all online ad revenue. In a Cost-per-click (CPC) system, advertisers pay a set rate each time the consumer clicks on the ad. Cost-per-click (CPC) systems are often associated with bidding markets, in which the advertiser bids against other advertisers for how much they're willing to pay per click. Most CPC ad revenue today comes from keyword bidding, in which advertisers bid for clicks from ads attached to particular keywords.
Compared to CPM pricing, CPC pricing more closely aligns the interests of the advertiser, the publisher, and the consumer, since the ads that are more relevant to consumers get higher click rates and thus higher CPC prices, and thus advertisers pay prices that more closely match the actual value delivered by the ads.
In principle, an advertiser who fully tracks ad clicks and conversions from clicks to purchases may not care whether they paid per impression or per click, since they would know precisely the value of each. But in practice, most advertisers still don't do such full tracking, and thus they rely on click rates as approximations for the relevance of an ad.
CPC pricing still suffers a serious problem; clicks originating from different Web sites in an ad network have different values to a publisher, but the publisher must pay a single, uniform CPC price, regardless of where the ad is displayed.
For example, an advertiser of genealogical services has found that users who click on an ad placed at AOL.com are much more likely to subscribe to the services than users who click on the same ad at Google.com, because the demographics of the users at the two sites are so different. However, the advertiser must pay the same price for those clicks, and thus may be underpaying for the clicks at AOL.com and/or overpaying for the clicks at Google.com.
Such systems are therefore economically inefficient, and greatly affect the relevance of the ads actually shown to consumers. Conventional ad networks typically use a bidded CPC as a primary determiner of which ads get shown; the higher the CPC, the more likely an ad will be shown. Thus, in the example above, the genealogy ads on AOL.com are not shown as much as they should be, because the advertiser is paying less per click than their true value to the advertiser. Similarly, the ads on Google.com are shown more than they should because the advertiser is paying more per click than their true value to the advertiser.
To address this problem, a conventional ad network could, in principle, allow advertisers to pay different CPC's for each of the thousands of publishers' sites in its network. However, such an approach makes the bidding even more complicated than it already is, and depresses bid prices, by subdividing the bidding market, allowing advertisers to pick and choose which sites run their ads, with far fewer advertisers bidding for a given keyword in any one submarket.
Similarly, uniform CPC pricing is not a good fit with behavioral targeting of ads. In behavioral targeting, ads are targeted to individual users based on their demographics and past behavior, such as which pages they've recently visited and what they've purchased. Typically, users may be segmented into dozens or hundreds of segments, while ads are differentially targeted to the segments. Since some segments are more likely to purchase after clicking on an ad than other segments, advertisers want to pay a different CPC for each segment. But, as discussed above, that greatly complicates an already complicated bidding process, and depresses bid prices.
As well, CPC pricing is very susceptible to “click fraud”. Using simple off the shelf software, an unscrupulous business can easily generate large volumes of fake clicks on its competitor's ads, forcing the competitor to pay for clicks that aren't generating any real business. More seriously, an unscrupulous publisher can run CPC ads from an ad network and create fake clicks in an attempt to get more revenue from the ad network (which shares its revenue with its publishers). Click fraud is widely recognized as a serious problem, and ad networks like Google and Yahoo invest large amounts to attempt to detect and prevent such fraud.
Complexity of Keyword Bidding.
Ad networks such as Google and Overture require advertisers to pick keyword phrases that control when the ad is shown and a maximum per-click price for each keyword phrase. Both tasks are quite difficult for the average advertiser.
The keyword phrases associated with an ad trigger when the ad is displayed to a user. On a search engine, the phrases are matched against the users' queries, and on Web pages, they are matched against the content of the pages. Picking good keywords is essential for making the ads relevant to the end users.
A single ad for one product might require dozens of triggering keyword phrases. A typical small advertiser might have dozens of ads and hundreds of keyword phrases, and medium and large-sized advertisers could have tens or hundreds of thousands of phrases. It's quite difficult for the typical advertiser to think of all the different phrases that should trigger the display of an ad. For example, a single ad for “Apple ipod” might require the following keyword phrases: “Apple ipod”, “iPod”, “mp3 player”, “Apple”, “Apple mp3 player”, “music player”, “Apple music player”, “portable music player”, “music appliance”, and similar keyword phrases for all of the iPod's competitors.
The advertiser then needs to pick the maximum cost-per-click she's willing to pay for each different keyword phrase. Typically, a sophisticated advertiser will measure the rate at which people who click on an ad triggered by a given keyword phrase go on to make a purchase—the so-called “conversion rate”. Knowing the conversion rate for a keyword phrase and the maximum amount the advertiser is willing to pay for a purchase of the advertised product or service, the advertiser can then choose the maximum CPC that is profitable for clicks on that keyword phrase. For example, suppose an advertiser is willing to pay $10 to acquire a purchase of one of its products, and suppose that 10% of users who click on an ad triggered by a given keyword phrase actually go on to make a purchase. In this case, the advertiser would be willing to pay up to $1 for each click.
In practice, most advertisers find it very difficult to pick thousands of keyword phrases, track conversion rates on them, and adjust the maximum CPCs accordingly. The advertisers' ads, products, prices, and Web site—all of which affect conversion rates—are constantly changing, and a sophisticated advertiser will constantly monitor and adjust keyword phrases and CPCs. However, as reported by JupiterResearch, at Search Engine Strategies Conference, 13-16 Dec. 2004, Chicago, Ill., “Only one of four search marketers bids and measures intelligently”, i.e. 3 of 4 of search marketers currently use unsophisticated search engine marketing (SEM) tactics.
The Use of Click Rate for Judging Ad Relevance.
A number of ad networks, including Google, use the rate of clicks on an ad as a partial measure of the ad's relevance to users. While this has worked well when the ads were shown mostly on a few search engines, it doesn't work nearly as well when the ads are shown on thousands of Web sites, and it doesn't work well with behavioral targeting.
Some conventional ad networks choose which ads to show on a page, by first finding ads whose keywords match the text on the page. Then the network ranks those matching ads, by estimating the effective revenue per impression it would get from each ad if it were to be shown on that page, and then picks the ads with the highest revenue per impression, referred to as either “effective CPM” or ECPM.
Google estimates the effective revenue per impression using the click rate of the ad and the bid price of the ad's keywords:ECPM=click rate of the ad*bid price per click for the ad keywords
“Click rate” is defined as clicks per impression, and “bid price per click” is defined as dollars per click, so effective revenue per impression is thus:ECPM=dollars/impression=clicks/impression*dollars/click
To measure click rate, such an ad network may run thousands of initial test impressions of an ad to get an accurate measure of a click rates (which are typically on the order of 0.1 to 1% for non-search ads). While the use of test impressions may work adequately on a single search engine, such systems quickly become cumbersome when applied to a network of thousands of publishers, or when applied to behavioral targeting.
The click rate for ads often varies considerably from one publisher's site to the next, from one section of a site to the next, and from one page to another. Similarly, click rates can vary significantly among the hundreds of user segments inferred by behavioral targeting.
It would therefore be advantageous to provide an ad network that could measure an ad's click rate separately for each of the thousands of likely combinations of publishers, sections within sites, pages within sections, and user segments, wherein such an ad network could optimize the ranking of ads for each different combination of ad, page, and user segment.
However, in practice, getting a separate measurement of click rate for each combination isn't practical. Any given page may be read by hundreds of segments of users and there may be hundreds to tens of thousands of reasonably likely ads for that page, so millions of test impressions of that page would be required just to measure click rate. Not only would it take too long to run those test impressions, e.g. perhaps weeks, but it would also cost too much, since such a system would be sacrificing significant revenue by running too many ads that generate too few clicks. And on smaller Web sites with fewer visitors, there simply aren't enough impressions available.
Thus, when estimating ECPM to select ads, conventional ad networks are limited to using the average click rate of ads over the entire network or large subsets of the network, rather than for each combination of page and user segment. Since actual click rates can vary greatly across combinations, the use of average click rate yields an inferior selection of ads for any given page and user.
As a consequence of their reliance on average click rates, conventional ad networks will find it difficult to introduce behavioral targeting. Such conventional ad networks also find it difficult to accommodate advertisers with very large numbers of ads, since each ad consumes test impressions in order to measure their click rates.
Features for Judging Relevance.
Current advertising technologies are quite limited in how they match ads with Web pages and users. There are four main approaches:
Matching the Demographics of the Buyers with that of the Audience.
For example, an advertiser of video games may preferably run its ads on sites whose audiences have a disproportionate number of 18-25 year-old males. Sometimes this matching employs quantitative data obtained from providers like comScore, but often it is intuitive. For example, movie advertisers typically run their ads in the entertainment section of a Web site.
Matching the Text of the Ad's Keywords with the Text of the User's Search Query or the Web Page.
For example, an advertiser of Apple iPods will likely have purchased the keyword “iPod”, and its ad will run on pages that contain the keyword “iPod” and on search results for user queries containing “iPod”.
Observed Click Rates.
As discussed above, conventional ad networks typically observe the actual click rate on an ad to judge its relevance.
Observed Past Behavior of Users.
With behavioral targeting, the past behavior of an individual user is used to predict to which ads a user is likely to respond. For example, a user who has visited auto-buying sites frequently in the past month may be more likely to respond to ads for auto loans.
Any one ad network typically uses just one or two of these approaches. Large CPM brand advertisers still rely primarily on matching demographics. For example, Advertising.com relies primarily on observed click rates. Tacoda, Revenue Science, and Claria rely on past behavior. Google relies on text matching with keywords purchased by the advertiser and on click rate.
In contrast to such conventional ad networks, current state-of-the-art consumer search engines typically use dozens of features to judge the relevance of indexed pages to a user's query. For example, search engines such as Yahoo and Google use many different measures of text match between the query and the different parts of the entire indexed pages and the text of links that point at the pages. Such search engines use numerous measures of the number and quality of incoming Web links, and may even use click rates to help identify more popular documents.
A modern search engine may have 50 or more such variables for judging relevance, as compared to the handful of variables used by ad technology. As a result, the results yielded by such search engines are often significantly more relevant than that of the accompanying text ads.
While the disclosed prior art systems and methodologies provide placement of ads within web sites based on a variety of pricing methods, the ads often have limited relevance to customers, and require significant effort and expertise from advertisers, thereby minimizing the value of the ads to advertisers, publishers, customers and the ad network.
It would be advantageous to provide a network ad network that combines state-of-the-art search technology with a radically different pricing model, wherein ads are much more relevant to consumers, much simpler and more effective for advertisers, and thus more profitable for publishers. The development of such a system would constitute a major technological advance.
In addition, it would be advantageous to provide an advertising system across a network, that analyzes both publisher content and advertiser content, past user behavior, profile information of users, past rates of performance of ads, time of day and day of week, and/or many other factors to determine relevance of ads to be displayed with publisher content, wherein the relevance is based on a prediction of response by the user. Furthermore, it would be advantageous to select one or more of the ads for display with the publisher content based on such a prediction. The development of such a system would constitute a further technological advance.